1. Field of the Invention
The present invention generally relates to feature extraction and, more particularly, to automatic setting of a decision threshold in apparatus for bi-level image processing.
2. Description of the Prior Art
In the manufacture of electrical components or complete circuits of substantial complexity, testing of continuity or other electrical parameters has become an integral part of the manufacturing process. In circumstances where large numbers of such circuits or components are manufactured, it is common to automate the testing process which is carried out at one or more points during the manufacturing process. For this purpose, test probes will often be placed on a specially constructed test jig corresponding to the circuit or component to be tested.
As the size of electrical components and circuits has been reduced in the normal course of developing technology, the density of test points has been increased and the size of test points has been reduced. It is not uncommon for contact and connection points to be many times the size of circuit elements in very large scale integration (VLSI) circuits and the value of the "footprint" occupied by a test point has risen greatly. For this reason, also, it has been necessary to reduce the size of test points in electronic devices and circuits.
The reduction in size of test points requires extremely high accuracy in the construction of a test jig corresponding to the device or test probes of correspondingly small size and high uniformity. Such precision does not present a technical problem. However, in use, the test jig (or individual probes if a test jig is not used) must be positioned to a similar degree of accuracy in two coordinate directions and, possibly, rotation. This positioning accuracy is complicated by the fact that dimensional variations will exist from part to part and the positioning accuracy of the part itself must be accommodated. Further, the speed of automated manufacturing machinery and potential throughput capacities preclude manual positioning operations. Therefore, it is common to automate the positioning process and to use feature recognition techniques to control the positioning of test jigs and probes in automated testing apparatus.
Feature recognition basically involves the sensing of reflected or transmitted radiation, possibly in the visible spectrum, while scanning over an area and detecting contrast between adjacent locations. However, this process is often complicated by the radiation reflective or transmissive properties of the materials used in the fabrication of the device. Rather than sensing radiation levels which will unambiguously indicate the presence or absence of a feature, the radiation levels sensed will more typically constitute a grey scale of values which must be converted to binary values based on a decision threshold value. While contrast can often be enhanced somewhat by choice of radiation wavelength, the setting of a threshold for determination of whether or not a certain sensed level of radiation represents a feature is critical to the operation. Also, the process of feature recognition at high resolution is subject to noise, making the decision threshold level choice particularly critical.
The choice of decision threshold level (hereinafter simply threshold level) is also complicated by the fact that radiation transparency or reflectivity will often vary from part to part and from point to point within a feature (constituting noise) by an amount which is significant when compared to the desired threshold. This implies that a threshold should be established for each part tested. This also implies that the part to part threshold adjustment must be very accurate in order to properly discriminate data from noise. Manual adjustment is precluded for the same reasons that manual positioning of the test jig or probes is not feasible. In fact, in multi-level ceramic (MLC) structures, a positioning accuracy of 6 microns is required at a high rate of speed. In order to avoid limitation of throughput of manufacturing equipment for such devices, it is necessary to accurately carry out feature recognition at a rate of under two seconds per feature recognized. Therefore, to reliably carry out the feature recognition process, the optimization of decision threshold must be carried out in a very small fraction of that interval to allow time for the feature to be recognized from the sensed data and for the positioning of the test probes.
Many arrangements for providing adaptive circuits and automatic setting of threshold levels exist in the electrical arts, in general. However, a particularly difficult problem is encountered in feature recognition. Assuming the sensed signal is slightly greater than the noise, a characteristic histogram plot of frequency of occurrence against signal level will show two maxima, one of which is very high, with a slight minimum between the maxima. The maxima will, of course correspond to the radiation levels sensed for background and features at respective locations on the scanned area. It is common for the other maximum of such a histogram plot to have a very low level which is only slightly greater than the background noise level. The disproportionate height of the peaks of the histogram will vary with the relative areas corresponding to features and background. Since, in at least the area of electronic devices, the feature area will generally correspond to the amount of material used, such as gold, it follows that economy dictates minimization of feature area and the slightness of the minimum of the histogram referred to above relative to the peak corresponding to features is extremely common and noise will be of a comparable level to the smaller maximum in virtually all instances.
While the above difficulties of feature recognition are particularly critical in supporting automated testing applications, feature recognition is often applied to numerous manufacturing processes requiring determination of the precise location of a structure, such as automatic assembly of components and many applications involving servomechanisms and robotics. Accordingly, the ability to optimize a decision threshold is necessary to efficient and accurate operation of a wide range of processes, including motion detection, low-light vision arrangements, medical imaging, land mass mapping, facsimile and copier machines where enhancement of contrast of a spatial image may improve the sensitivity or performance of the process. In view of the variation of noise levels, and the levels of radiation sensed from background and feature areas during feature recognition, it is desirable that threshold levels be adaptively set.